Impact Assessment of Land-Use Changes on Nutrient Load in Song Cau Watershed Using Soil and Water Assessment Tool （SWAT） Model

The objective of this paper is to implement ＂Soil and Water Assessment Tool （SWAT）＂ model to assess the possible impact of land-use changes on nutrient yields from Song Cau watershed located in Northern Viet Nam. Organic nitrogen （N） as well as phosphorus （P） output due to nonpoint source erosion was estimated through SWAT. Parameters governing the mechanics of streamflow discharge, sediment yield, nitrogen, and phosphorus output in SWAT were calibrated in a distributed fashion. A five-year period of record for nutrient was used for model calibration, while a four-year period was used for model validation. Comparing measured versus simulated average monthly total N, and P loads for the calibration and validation periods; respectively, we found that SWAT model performed reasonably well for Song Cau watershed. Simulation results showed that monthly Nash-Sutcliffe coefficient of Efficiency （NSE） ranged from 0.65 to 0.83, observation＇s standard deviation ratio （RSR） and percent bias （PBIAS） ranged from 0.41 to 0.58 and -36.12 to 2.78, respectively. Additionally, SWAT simulation results also showed that land-use changes caused significant percentage of changes in sediment yield, total N, and P loads within Song Cau watershed.

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

Long Term Soil and Water Assessment Tool (SWAT) Calibration from an Ecohydrology Perspective

The performance on prediction by mathematical models which represent the conceived image of a system such as hydrology is oftentimes represented through calibration and verification processes. Oftentimes a best fit between observed and predicted flows is obtained through correlation coefficient (R2) and the Nash Sutcliffe model efficiency (NSE) by minimizing the average Root Mean Square Error (RMSE) of the observed versus simulated flows. However, these days, a new paradigm is emerging wherein accounting for the flow variability for the protection of freshwater biodiversity and maintenance of goods and services that rivers provide is paramount. Therefore, from an ecohydrology perspective, it is not clear if the existing method of model calibration meets the needs of the riverine ecosystem at its best. Thus, this study investigates and proposes a methodology using entropy theory to gage the calibration of Soil and Water Assessment Tool (SWAT) from an ecohydrology perspective characterized by the natural flow-regime paradigm: Indicators of Hydrologic Alteration.

Combining CLUE-S and SWAT Models to Forecast Land Use Change and Non-point Source Pollution Impact at a Watershed Scale in Liaoning Province, China

Non-point source(NPS) pollution has become a major source of water pollution. A combination of models would provide the necessary direction and approaches designed to control NPS pollution through land use planning. In this study, NPS pollution load was simulated in urban planning, historic trends and ecological protection land use scenarios based on the Conversion of Land Use and its Effect at Small regional extent(CLUE-S) and Soil and Water Assessment Tool(SWAT) models applied to Hunhe-Taizi River Watershed, Liaoning Province, China. Total nitrogen(TN) and total phosphorus(TP) were chosen as NPS pollution indices. The results of models validation showed that CLUE-S and SWAT models were suitable in the study area. NPS pollution mainly came from dry farmland, paddy, rural and urban areas. The spatial distribution of TN and TP exhibited the same trend in 57 sub-catchments. The TN and TP had the highest NPS pollution load in the western and central plains, which concentrated the urban area and farm land. The NPS pollution load would increase in the urban planning and historic trends scenarios, and would be even higher in the urban planning scenario. However, the NPS pollution load decreased in the ecological protection scenario. The differences observed in the three scenarios indicated that land use had a degree of impact on NPS pollution, which showed that scientific and ecologically sound construction could effectively reduce the NPS pollution load in a watershed. This study provides a scientific method for conducting NPS pollution research at the watershed scale, a scientific basis for non-point source pollution control, and a reference for related policy making.

A GIS-based Modeling Approach for Fast Assessment of Soil Erosion by Water at Regional Scale, Loess Plateau of China

The objective of this study is to develop a unique modeling approach for fast assessment of massive soil erosion by water at a regional scale in the Loess Plateau, China. This approach relies on an understanding of both regional patterns of soil loss and its impact factors in the plateau area. Based on the regional characteristics of precipitation, vegetation and land form, and with the use of Landsat TM and ground investigation data, the entire Loess Plateau was first divided into 3 380 Fundamental Assessment Units （FAUs） to adapt to this regional modeling and fast assessment. A set of easily available parameters reflecting relevant water erosion factors at a regional scale was then developed, in which dynamic and static factors were discriminated. Arclnfo GIS was used to integrate all essential data into a central database. A resulting mathematical model was established to link the sediment yields and the selected variables on the basis of FAUs through overlay in GIS and multiple regression analyses. The sensitivity analyses and validation results show that this approach works effectively in assessing large area soil erosion, and also helps to understand the regional associations of erosion and its impact factors, and thus might significantly contribute to planning and policymaking for a large area erosion control in the Loess Plateau.

Integrating water use systems and soil and water conservation measures into a hydrological model of an Iranian Wadi system

Water resources are precious in arid and semi-arid areas such as the Wadis of Iran. To sustainably manage these limited water resources, the residents of the Iranian Wadis have been traditionally using several water use systems(WUSs) which affect natural hydrological processes. In this study, WUSs and soil and water conservation measures(SWCMs) were integrated in a hydrological model of the Halilrood Basin in Iran. The Soil and Water Assessment Tool(SWAT) model was used to simulate the hydrological processes between 1993 and 2009 at daily time scale. To assess the importance of WUSs and SWCMs, we compared a model setup without WUSs and SWCMs(Default model) with a model setup with WUSs and SWCMs(WUS-SWCM model). When compared to the observed daily stream flow, the number of acceptable calibration runs as defined by the performance thresholds(Nash-Sutcliffe efficiency(NSE)≥0.68, –25%≤percent bias(PBIAS)≤25% and ratio of standard deviation(RSR)≤0.56) is 177 for the Default model and 1945 for the WUS-SWCM model. Also, the average Kling–Gupta efficiency(KGE) of acceptable calibration runs for the WUS-SWCM model is higher in both calibration and validation periods. When WUSs and SWCMs are implemented, surface runoff(between 30% and 99%) and water yield(between 0 and 18%) decreased in all sub-basins. Moreover, SWCMs lead to a higher contribution of groundwater flow to the channel and compensate for the extracted water by WUSs from the shallow aquifer. In summary, implementing WUSs and SWCMs in the SWAT model enhances model plausibility significantly.

A Comparison of SWAT Model Calibration Techniques for Hydrological Modeling in the Ganga River Watershed

The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water. In this study, hydrological modeling was applied to Soil and Water Assessment Tool （SWAT） modeling in the Ganga catchment, over a region of 15 621.612 km2 in the southern part of Uttar Pradesh. The primary goals of this study are： ① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield; and ② to compare and determine the best calibration algorithm among three popular algorithms-sequential uncertainty fitting version 2 （SUFI-2）, the generalized likelihood uncertainty estimation （GLUE）, and par-allel solution （ParaSol）. The input data used in the SWAT were the Shuttle Radar Topography Mission （SRTM） digital elevation model （DEM）, Landsat-8 satellite imagery, soil data, and daily meteorological data. The watershed of the study area was delineated into 46 sub-watersheds, and a land use/land cover （LULC） map and soil map were used to create hydrological response units （HRUs）. Models utilizing SUFI- 2, GLUE, and ParaSol methods were constructed, and these algorithms were compared based on five cat-egories： their objective functions, the concepts used, their performances, the values of P-factors, and the values of R-factors. As a result, it was observed that SUFI-2 is a better performer than the other two algo-rithms for use in calibrating Indian watersheds, as this method requires fewer runs for a computational model and yields the best results among the three algorithms. ParaSol is the worst performer among the three algorithms. After calibrating using SUFI-2, five parameters including the effective channel hydraulic conductivity （CH_K2）, the universal soil-loss equation （USLE） support parameter （USLE_P）, Manning＇s n value for the main channel （CH_N2）, the surface runoff lag time （SURLAG）, and the available water capac-ity of the soil layer （SOL_AWC） were observed to be the most sensitive parameters for modeling the pre-sent watershed. It was also found that the maximum runoff occurred in sub-watershed number 40 （SW#40）, while the maximum sediment yield was 50 t.a ^1 for SW#36, which comprised barren land. The average evapotranspiration for the basin was 411.55 mm.a ^1. The calibrated model can be utilized in future to facilitate investigation of the impacts of LULC, climate change, and soil erosion.

Use of SWAT to Model Impact of Climate Change on Sediment Yield and Agricultural Productivity in Western Oregon, USA

Climate change predictions for the Pacific Northwest region of the United States of America include increasing temperatures, intensification of winter precipitation, and a shift from mixed snow/rain to rain-dominant events, all of which may increase the risk of soil erosion and threaten agricultural and ecological productivity. Here we used the agricultural/environmental model SWAT with climate predictions from the Coupled Model Intercomparison Project 5 (CMIP5) “high CO2 emissions” scenario (RCP8.5) to study the impact of altered temperature and precipitation patterns on soil erosion and crop productivity in the Willamette River Basin of western Oregon. An ensemble of 10 climate models representing the full range in temperature and precipitation predictions of CIMP5 produced substantial increases in sediment yield, with differences between yearly averages for the final (2090-2099) and first (2010-2019) decades ranging from 3.9 to 15.2 MT·ha-1 among models. Sediment yield in the worst case model (CanESM2) corresponded to loss of 1.5 - 2.7 mm·soil·y-1, equivalent to potentially stripping productive topsoil from the landscape in under two centuries. Most climate models predicted only small increases in precipitation (an average of 5.8% by the end of the 21st century) combined with large increases in temperature (an average of 0.05°C·y-1). We found a strong correlation between predicted temperature increases and sediment yield, with a regression model combining both temperature and precipitation effects describing 79% of the total variation in annual sediment yield. A critical component of response to increased temperature was reduced snowfall during high precipitation events in the wintertime. SWAT characterized years with less than basin-wide averages of 20 mm of precipitation falling as snow as likely to experience severe sediment loss for multiple crops/land uses. Mid-elevation sub-basins that are projected to shift from rain-snow transition to rain-dominant appear particularly vulnerable to sediment loss. Analyses of predicted crop yields indicated declining productivity for many commonly grown grass seed and cereal crops, along with increasing productivity for certain other crops. Adaptation by agriculture and forestry to warmer, more erosive conditions may include changes in selection of crop kinds and in production management practices.

Estimation of Pollutant Loads in Ardila Watershed Using the SWAT Model

Abstract： Excess of organic matter and nutrients in water promotes eutrophication process observed in the Ardila River. It was classified as much polluted being critical for Alqueva-Pedrogāo System. The aim of this study was to estimate the transported nutrients loads in a transboundary watershed using the SWAT （soil and water assessment tool） model and to determine the contribution of nutrients load in the entire watershed. Ardila watershed is about 3,711 km^2 extended from Spain （78%） to the eastern part of Portugal （22%）. It was discretized into 32 sub-basins using automated delineation routine, and 174 hydrologic response units. Monthly average meteorological data （from 1947 to 1998） were used to generate daily values through the weather generator Model incorporated in SWAT. Real daily precipitation （from 1931 to 2003） was introduced. The model was calibrated and verified for flow （from 1950 to 2000） and nutrients （from 1981 to 1999）. Model performance was evaluated using statistical parameters, such as NSE （Nash-Sutcliffe efficiency） and root mean square error （R2）. Calibration and verification flow results showed a satisfactory agreement between simulated and measured monthly date from 1962 to 1972 （NSE = 0.8; R^2 = 0.9）. The results showed that the most important diffuse pollution comes from the two the main tributary （Spain）. The estimated nitrogen and phosphorous loads contribution per year was respectively 72% and 59% in Spain and 28% and 41% in Portugal. The SWAT model was revealed to be a useful tool for an integrated water management approach that might be improved taking into count the WFD （water framework directive）.

基于RBF-SWAT的气候变化下汉江上游流域径流预测及特征分析

为研究气候变化下汉江上游流域的径流响应,基于RBF神经网络降尺度模型,利用2020—2099年CanESM2模式下RCP8.5(高温室气体排放)和RCP2.6(低温室气体排放)两种气候情景,生成未来气温与降水数据;耦合SWAT水文模型,模拟分析流域2020—2099年径流变化对不同气候情景的响应特征。结果表明,汉江上游年径流量均呈不明显增加趋势,RCP8.5情景下的径流增加趋势比RCP2.6情景稍小,径流量年内分配与基准期大致相同,两种情景下汛期径流量稍有减小,可能是降尺度模型生成的降水量极大值偏小导致的。研究结果可为汉江流域水文气象综合管理提供一定的科学依据。

Simulation of hydrological processes of mountainous watersheds in inland river basins: taking the Heihe Mainstream River as an example

The hydrological processes of mountainous watersheds in inland river basins are complicated.It is absolutely significant to quantify mountainous runoff for social,economic and ecological purposes.This paper takes the mountainous watershed of the Heihe Mainstream River as a study area to simulate the hydrological processes of mountainous watersheds in inland river basins by using the soil and water assessment tool(SWAT)model.SWAT simulation results show that both the Nash–Sutcliffe efficiency and the determination coefficient values of the calibration period(January 1995 to December 2002)and validation period(January 2002 to December 2009)are higher than 0.90,and the percent bias is controlled within±5%,indicating that the simulation results are satisfactory.According to the SWAT performance,we discussed the yearly and monthly variation trends of the mountainous runoff and the runoff components.The results show that from 1996 to 2009,an indistinctive rising trend was observed for the yearly mountainous runoff,which is mainly recharged by lateral flow,and followed by shallow groundwater runoff and surface runoff.The monthly variation demonstrates that the mountainous runoff decreases slightly from May to July,contrary to other months.The mountainous runoff is mainly recharged by shallow groundwater runoff in January,February,and from October to December,by surface runoff in March and April,and by lateral flow from May to September.

A Modified Groundwater Module in SWAT for Improved Streamflow Simulation in a Large, Arid Endorheic River Watershed in Northwest China

Interactions between surface water and groundwater are dynamic and complex in large endorheic river watersheds in Northwest China due to the influence of both irrigation practices and the local terrain. These interactions interchange numerous times throughout the middle reaches, making streamflow simulation a challenge in endorheic river watersheds. In this study, we modified the linear-reservoir groundwater module in SWAT(Soil and Water Assessment Tools, a widely used hydrological model) with a new nonlinear relationship to better represent groundwater processes; we then applied the original SWAT and modified SWAT to the Heihe River Watershed, the second largest endorheic river watershed in Northwest China, to simulate streamflow. After calibrating both the original SWAT model and the modified SWAT model, we analyzed model performance during two periods: an irrigation period and a non-irrigation period. Our results show that the modified SWAT model with the nonlinear groundwater module performed significantly better during both the irrigation and non-irrigation periods. Moreover, after comparing different runoff components simulated by the two models, the results show that, after the implementation of the new nonlinear groundwater module in SWAT, proportions of runoff components changed-and the groundwater flow had significantly increased, dominating the discharge season. Therefore, SWAT coupled with the non-linear groundwater module represents the complex hydrological process in the study area more realistically. Moreover, the results for various runoff components simulated by the modified SWAT models can be used to describe the hydrological characteristics of lowland areas. This indicates that the modified SWAT model is applicable to simulate complex hydrological process of arid endorheic rivers.

Quantification of Effects of Natural Geographical Factors and Land-scape Patterns on Non-point Source Pollution in Watershed Based on Geodetector:Burhatong River Basin,Northeast China as An Example

Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed;however,to slow down non-point source pollution,it is necessary to distinguish their effects.But the non-point source pollution process is interactional as a result of multiple factors,and the collinearity between multiple independent variables limits our ability of reason diagnosis.Thus,taking the Burhatong River Basin,Northeast China as an example,the methods of hydrological simulation,geographic detectors,and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed.The Soil&Water Assessment Tool(SWAT)has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed.The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index(LWLI)are the main indicators influencing the rivers total nitrogen and total phosphorus.The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen(TN)and total phosphorus(TP).Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons.TN and TP loads are affected mainly by the change in landscape pattern,especially in the wet season.Although the ecological restoration program has improved forest coverage,the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation.The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.

Defining Soil and Water Assessment Tool(SWAT)hydrologic responseunits(HRUs)by field boundaries

The Soil and Water Assessment Tool(SWAT)is widely used to relate farm management practices to their impacts on surface waters at the watershed scale,yet its smallest spatial unit is not generally defined by physically meaningful boundaries.The hydrologic response unit(HRU)is the smallest spatial unit of the model,and the standard HRU definition approach lumps all similar land uses,soils,and slopes within a subbasin based upon user-defined thresholds.This standard method provides an efficient way to discretize large watersheds where simulation at the field scale may not be computationally feasible.In relatively smaller watersheds,however,defining HRUs to specific spatial locations bounded by property lines or field borders would often be advantageous,yet this is not currently possible within the ArcSWAT interface.In this study,a simple approach is demonstrated that defines HRUs by field boundaries through addition of uniquely named soils to the SWAT user soil database and creation of a field boundary layer with majority land use and soil attributes.Predictions of nitrogen,phosphorus,and sediment losses were compared in a case study watershed where SWAT was set up using both the standard HRU definition and field boundary approach.Watershed-scale results were reasonable and similar for both methods,but aggregating fields by majority soil type masked extremely high soil erosion predicted for a few soils.Results from field-based HRU delineation may be quite different from the standard approach due to choosing a majority soil type in each farm field.This approach is flexible such that any land use and soil data prepared for SWAT can be used and any shapefile boundary can divide HRUs.

基于SWAT模型的图们江流域蓝绿水资源供需平衡分析

随着经济快速发展和工农业各类用水量大幅增加,对河流的开发程度随之加强,流域水资源可持续利用受到不利影响。从蓝绿水视角出发,基于SWAT模型量化了图们江流域2015—2020年不同时间尺度下的蓝绿水资源并探讨了研究区水资源供需平衡情况。结果表明:研究时段内流域蓝绿水资源均具有明显的季节特征,年均蓝绿水资源量为117.16亿m^(3),其中蓝水资源量30.14亿m^(3),绿水资源量87.02亿m^(3),绿水占水资源量的74.27%。以年为尺度,蓝水资源供需评估指数>1,可利用蓝水资源量基本不能满足其需求量,降水少的年份蓝水资源供需矛盾突出,而绿水资源可以达到供需平衡,其供需评估指数集中在0.1—0.2之间。在月尺度分析中研究区蓝水供需评估指数出现极端值的频率较高,表明该地区蓝水供需不平衡,尤其是可利用蓝水资源一般在每年的7—8月才较为丰富,而蓝水需求用水集中分布于作物生长期,导致蓝水供应与蓝水需求在时间分配上不均衡,且在实际水资源分配中忽略了生态用水;月尺度绿水供需评估指数集中在0.1—0.4之间,有明显的季节变化特征,整体上绿水资源供需平衡安全性较高。

基于SWAT模型的济南市地下水资源评价

济南市地下水资源紧缺,合理开发利用地下水资源,对生态环境保护至关重要。本文采用分布式水文模型SWAT对济南市典型流域地下水分布规律进行分析,对地下水资源量进行评价。研究结果显示:2014~2018年,典型流域地下水补给量68.13 mm,地下水补给资源量2.74×10^(8)m^(3)。降水是地下水补给的主要水源,不同时间尺度下,降水与地下水补给量均具有相关性,年尺度下,R2达到0.95。2014~2018年,地下水补给资源量呈增加趋势,其中,特枯年0.40×10^(8)m^(3),枯水年1.44×10^(8)m^(3),平水年4.01×10^(8)m^(3)。受地形与水文地质条件影响,地下水补给量与地下水补给资源量在空间上呈聚合分布。研究结果对于济南市地下水资源可持续开发利用具有指导意义。

Application of SWAT99.2 to sensitivity analysis of water balance components in unique plots in a hilly region

Although many sensitivity analyses using the soil and water assessment tool(SWAT) in a complex watershed have been conducted, little attention has been paid to the application potential of the model in unique plots. In addition, sensitivity analysis of percolation and evapotranspiration with SWAT has seldom been undertaken. In this study, SWAT99.2 was calibrated to simulate water balance components for unique plots in Southern China from 2000 to 2001, which included surface runoff, percolation, and evapotranspiration. Twenty-one parameters classified into four categories, including meteorological conditions, topographical characteristics, soil properties, and vegetation attributes, were used for sensitivity analysis through one-at-a-time(OAT) sampling to identify the factor that contributed most to the variance in water balance components. The results were shown to be different for different plots, with parameter sensitivity indices and ranks varying for different water balance components. Water balance components in the broad-leaved forest and natural grass plots were most sensitive to meteorological conditions, less sensitive to vegetation attributes and soil properties, and least sensitive to topographical characteristics. Compared to those in the natural grass plot, water balance components in the broad-leaved forest plot demonstrated higher sensitivity to the maximum stomatal conductance(GSI) and maximum leaf area index(BLAI).

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Impacts of Land Cover Changes on Runoff and Sediment in the Cedar Creek Watershed,St.Joseph River,Indiana,United States

The relation between runoff and sediment and land cover is investigated in the Cedar Creek Watershed （CCW）, located in Northeastern Indiana, United States. The major land cover types in this watershed are cultivated land, woodland and pasture /Conservation Reserve Program （CRP）, which account for approximate 90 % of the total area in the region. Moreover, land use was changed tremendously from aooo to 9004, even without regarding the effect of the crop rotation system （corn ＆ soybean）. At least 49 % of land cover types were changed into other types in this period. The land cover types, ranking by changing area from high to low series, are rye, soybean, corn, woodland and pasture/CRP. The CCW is divided into 21 subwatersheds, and soil and water loss in each sub-watershed is computed by using Soil and Water Assessment Tool （SWAT）. The results indicate that the variations in runoff and sediment have positive relation to the area of crops （especially corn and soybean）; sediment is more sensitive to land cover changes than runoff; more heavy rainfall does not always mean more runoff because the combination of different land cover types always modify runoff coefficient; and rye, soybean and corn are the key land cover types, which affected the variation in runoff and sediment in the CCW.

基于SWAT模型的平原区地表水资源还原计算方法研究

平原区地势平坦、河网交错,受强人类活动干扰,水文过程复杂。研究基于SWAT模型开展平原区地表水资源还原方法研究,针对平原区地形平坦问题,提出了基于DEM和实际河网矢量图相结合的平原河网水系修正调整方法;针对高强度人类活动对自然下垫面的干扰,提出了基于就近性与相似性原则的SWAT模型参数展布方法;并以安徽省宿州市为背景,分析计算了研究区内各单元的地表水资源量,并与分项还原法计算结果比较。结果表明,SWAT模型模拟得到的多年平均地表水资源量比分项还原法多0.71亿m^(3),显示分项还原法有可能存在还原不充分问题。